Improved Emotion Recognition with Novel Task-Oriented Wavelet Packet Features

In this paper, a wavelet packet based adaptive filter-bank construction method is proposed for speech signal processing. On this basis, a set of acoustic features are proposed for speech emotion recognition, namely Wavelet Packet Cepstral Coefficients (WPCC). The former extends the conventional Mel-Frequency Cepstral Coefficients (MFCC) by adapting the filter-bank structure according to the decision task; while the later aims at selecting the most crucial frequency bands where the most discriminative emotion information is located. Speech emotion recognition system is constructed with the two proposed feature sets and Gaussian mixture model as classifier. Experimental results on Berlin emotional speech database show that the proposed features improve emotion recognition performance over the conventional MFCC feature. The proposed feature extraction scheme has encouraging prospects since it can be extended to 2D image processing with 2D wavelet packets and hence extended to audio-visual bimodal emotion recognition application.